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Considerable achievements in computing and telecommunication area make possible in a new way solve a wide spectrum of transportation problems, affected in the concept of intelligent transportation systems (ITS). In a technical aspect such sort systems represent a set of interacting computational nodes, equipped with various sensors, and can be trea...
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Bi-level optimization (BO) has become a fundamental mathematical framework for addressing hierarchical machine learning problems. As deep learning models continue to grow in size, the demand for scalable bi-level optimization solutions has become increasingly critical. Traditional gradient-based bi-level optimization algorithms, due to their inhere...
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... According to the definition, intelligent transportation systems are those utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems from different aspects. 3 An autonomous car provides more convenience and, safety and less energy-intensive to the driver. Current autonomous cars such as Tesla Model 3 and Google Self-Driving Car provide the functionality of "fully self-driven," meaning the car will drive by itself without requiring much driver input by relying on its advanced car sensor and computer vision camera. ...
This article presents a collision avoidance system for multiple robots based on the current autonomous car collision avoidance system. The purpose of the system is to improve the current autonomous car collision avoidance system by including data input of other vehicles’ velocity and positioning via vehicle-to-vehicle communication into the current autonomous car collision avoidance system. There are two TurtleBots used in experimental testing. TurtleBot is used as the robot agent while Google Lightweight Communication and Marshalling is used for inter-robot communication. Additionally, Gazebo software is used to run the simulation. There are two types of collision avoidance system algorithm (collision avoidance system without inter-robot communication and collision avoidance system with inter-robot communication) that are developed and tested in two main road crash scenarios, rear end collision scenario and junction crossing intersection collision scenario. Both algorithms are tested and run both in simulation and experiment setup, each with 10 repetitions for Lead TurtleBot sudden stop, Lead TurtleBot decelerate, Lead TurtleBot slower speed, and straight crossing path conditions. Simulation and experimental results data for each algorithm are recorded and tabulated. A comprehensive comparison of performance between the proposed algorithms is analyzed. The results showed that the proposed system is able to prevent collision between vehicles with an acceptable success rate.
